Create app.py

app.py

@@ -0,0 +1,65 @@
+import torch
+import gradio as gr
+import pandas as pd
+import matplotlib.pyplot as plt
+from transformers import pipeline
+
+analyzer = pipeline("text-classification",
+                model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")
+
+def sentiment_analyzer(review):
+    # Check if the review is a valid string
+    if pd.isna(review) or not isinstance(review, str):
+        return "NEUTRAL"  # Return neutral for invalid inputs
+    try:
+        sentiment = analyzer(review)
+        return sentiment[0]['label']
+    except:
+        return "NEUTRAL"  # Return neutral for any errors
+
+def sentiment_bar_chart(df):
+    sentiment_counts = df['Sentiment'].value_counts()
+    
+    # Create a bar chart
+    plt.figure(figsize=(10, 6))
+    plt.pie(sentiment_counts.values, labels=sentiment_counts.index, autopct='%1.1f%%', 
+            colors=['lightgreen', 'lightcoral', 'lightgray'])
+    plt.title('Review Sentiment Distribution')
+    return plt.gcf()
+
+def read_reviews_and_analyze_sentiment(file_object):
+    try:
+        # Load the Excel file into a DataFrame
+        df = pd.read_excel(file_object)
+        
+        # Check if 'Review' column is in the DataFrame
+        if 'Review' not in df.columns:
+            raise ValueError("Excel file must contain a 'Review' column.")
+        
+        # Convert Review column to string type and handle NaN values
+        df['Review'] = df['Review'].astype(str)
+        
+        # Apply the sentiment_analyzer function to each review
+        df['Sentiment'] = df['Review'].apply(sentiment_analyzer)
+        
+        # Create the chart
+        chart_object = sentiment_bar_chart(df)
+        
+        return df, chart_object
+    except Exception as e:
+        raise gr.Error(f"Error processing file: {str(e)}")
+
+# Create the Gradio interface
+demo = gr.Interface(
+    fn=read_reviews_and_analyze_sentiment,
+    inputs=[gr.File(file_types=["xlsx"], label="Upload your review comment file")],
+    outputs=[
+        gr.Dataframe(label="Sentiments"),
+        gr.Plot(label="Sentiment Analysis")
+    ],
+    title="@GenAILearniverse Project 3: Sentiment Analyzer",
+    description="THIS APPLICATION WILL BE USED TO ANALYZE THE SENTIMENT BASED ON FILE UPLOADED."
+)
+
+if __name__ == "__main__":
+    demo.launch()